System and method for producing a control signal of an electric vertical take-off and landing (eVTOL) aircraft

ABSTRACT

A system for producing a control signal of an electric vertical take-off and landing (eVTOL) aircraft includes a flight controller configured to obtain a requested aircraft force, generate an optimal command mix, wherein the optimal command mix includes a plurality of commands to a plurality of actuators as a function of the requested aircraft force, wherein generating further comprises receiving an ideal actuator model includes at least a performance parameter, producing a model datum as a function of the ideal actuator model, and generating the optimal command mix as a function of the request aircraft force and the model datum, and produce a control signal as a function of the optimal command mix.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Non-provisional application Ser.No. 17/383,572 filed on Jul. 23, 2021 and entitled “SYSTEM AND METHODFOR PRODUCING A CONTROL SIGNAL OF AN ELECTRIC VERTICAL TAKE-OFF ANDLANDING (EVTOL) AIRCRAFT,” the entirety of which is incorporated hereinby reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircrafts. In particular, the present invention is directed to a systemand method for producing a control signal of an electric verticaltake-off and landing (eVTOL) aircraft.

BACKGROUND

Aircrafts often operate inefficiently due to the large number of degreesof freedom that need to be accounted. Specifically, inefficiency isgenerated due to a poorly computed determination of one or more aircraftparameters such as position, speed, etc. This is further complicated bythe introduction of electric aircrafts that result in more degrees offreedom that are required to be determined.

SUMMARY OF THE DISCLOSURE

In an aspect, a method for producing a signal of an electric verticaltake-off and landing (eVTOL) aircraft, the method including obtaining,by a flight controller, a requested aircraft force, generating, by theflight controller, an optimal command mix, wherein the optimal commandmix includes a plurality of commands to a plurality of actuators as afunction of the requested aircraft force, wherein generating the optimalcommand mix further includes receiving an ideal actuator model includingat least a performance parameter, producing a model datum as a functionof the ideal actuator model, and generating the optimal command mix as afunction of the requested aircraft force and the model datum. The methodfurther including producing, by the flight controller, a control signalas a function of the optimal command mix, wherein the control signalcomprises a command directed to the plurality of actuators coupled to aplurality of flight components, respectively, of the eVTOL aircraft,wherein the command causes the eVTOL aircraft to perform a maneuver.

In another aspect, a system producing a signal of an electric verticaltake-off and landing (eVTOL) aircraft, the system including one or moreprocessors configured to obtain, by a flight controller, a requestedaircraft force, generate, by the flight controller, an optimal commandmix, wherein the optimal command mix includes a plurality of commands toa plurality of actuators as a function of the requested aircraft force,wherein generating the optimal command mix further includes receiving anideal actuator model including at least a performance parameter,producing a model datum as a function of the ideal actuator model, andgenerating the optimal command mix. The one or more processors furtherconfigured to produce, by the flight controller, a control signal as afunction of the optimal command mix, wherein the control signalcomprises a command directed to a plurality of actuators coupled to aplurality of flight components of the eVTOL aircraft, wherein thecommand causes the eVTOL aircraft to perform a maneuver.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of a system for producing a control signal ofan electric vertical take-off and landing (eVTOL) aircraft according toan embodiment of the invention;

FIG. 2 is a diagrammatic representation of an exemplary embodiment of aneVTOL aircraft;

FIG. 3 is a block diagram of an exemplary embodiment of a flightcontroller;

FIG. 4 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 5 , is a flow diagram illustrating a method for producing a controlsignal of an electric vertical take-off and landing (eVTOL) aircraftaccording to an embodiment of the invention;

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims. For purposes ofdescription herein, the terms “upper”, “lower”, “left”, “rear”, “right”,“front”, “vertical”, “horizontal”, and derivatives thereof shall relateto the invention as oriented in FIG. 1 . Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, brief summary or thefollowing detailed description. It is also to be understood that thespecific devices and processes illustrated in the attached drawings, anddescribed in the following specification, are simply exemplaryembodiments of the inventive concepts defined in the appended claims.Hence, specific dimensions and other physical characteristics relatingto the embodiments disclosed herein are not to be considered aslimiting, unless the claims expressly state otherwise.

At a high level, aspects of the present disclosure are directed tosystems and methods for producing a control signal of an electricvertical take-off and landing (eVTOL) aircraft. In an embodiment, thisdisclosure can obtain a requested aircraft force. Aspects of the presentdisclosure can also be used to generate an optimal command mix as afunction of the requested aircraft force. This is so, at least in part,because the disclosure includes a plurality of commands to a pluralityof plurality of actuators. Aspects of the present disclosure allow forproducing a control signal as a function of the optimal command mix.Exemplary embodiments illustrating aspects of the present disclosure aredescribed below in the context of several specific examples.

Referring now to FIG. 1 , an exemplary embodiment of a system 100 forinitiating a command of an electric vertical take-off and landing(eVTOL) aircraft is illustrated. System includes a flight controller104. As used in this disclosure a “flight controller” is a computingdevice of a plurality of computing devices dedicated to data storage,security, distribution of traffic for load balancing, and flightinstruction. Flight controller 104 may include and/or communicate withany computing device as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure.Further, flight controller 104 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. In embodiments, flight controller104 may be installed in an aircraft, may control the aircraft remotely,and/or may include an element installed in the aircraft and a remoteelement in communication therewith.

In an embodiment, and still referring to FIG. 1 , flight controller 104may include a reconfigurable hardware platform. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform may be reconfigured toenact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learning and/orneural net processes as described below.

Still referring to FIG. 1 , flight controller 104 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller 104 may communicatewith one or more additional devices as described below in further detailvia a network interface device. The network interface device may beutilized for commutatively connecting a flight controller to one or moreof a variety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 1 , flight controller 104may include, but is not limited to, for example, a cluster of computingdevices in a first location and a second computing device or cluster ofcomputing devices in a second location. Flight controller 104 mayinclude one or more computing devices dedicated to data storage,security, distribution of traffic for load balancing, and the like.Flight controller 104 may be configured to distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Flight controller 104 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft 100 and/or computing device.

In an embodiment, and with continued reference to FIG. 1 , flightcontroller 104 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 104 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller 104 may perform any step or sequenceof steps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 1 , flight controller 104 is configured toobtain a requested aircraft force 108. As used in this disclosure a“requested aircraft force” is a measurable value of force exerted on aflight component that is requested and/or desired, wherein a flightcomponent is described below. In an embodiment, and without limitation,requested aircraft force 108 may denote an expectation for a propellerto exert 160 lb. ft. of torque. As a further non-limiting example,desired torque may denote a request for a propulsor to exert 290 lb. ft.of torque. In an embodiment, and without limitation, requested aircraftforce 108 may be obtained as a function of a collective control. As usedin this disclosure a “collective control” is a mechanical control of anaircraft that allows a pilot to request an aircraft force. For example,and without limitation, collective control may include one or more hovercontrols, thrust controls, inceptor sticks, cyclic controls, yokecontrols, and the like thereof. As a further non-limiting example,collective control may include one or more roll sticks, rudder pedals,pitch sticks, throttle controls, and the like thereof. In an embodiment,and without limitation, collective control may control one or morecontrol surfaces, such as but not limited to rudders, ailerons,elevators, and the like thereof. In another embodiment, and withoutlimitation, collective control may include one or more nozzles,diverters, physical structures, vanes, and the like thereof.Additionally or alternatively, requested aircraft force may include apilot desire and/or pilot request to affect one or more speeds,directions, attitudes, orientations, and the like thereof of eVTOLaircraft.

In an embodiment, and still referring to FIG. 1 , requested aircraftforce 108 may be obtained as a function of a receiving a pilot input. Asused in this disclosure a “pilot input” is a signal and/or inputreceived from a pilot directed to maneuvering eVTOL aircraft. Forexample, pilot input may include one or more inputs received thatdirects a propulsor to increase a torque. As a further non-limitingexample, pilot input may include one or more inputs received that directan aileron to adjust an angle. In an embodiment, pilot input may includean implicit signal and/or an explicit signal. For example, and withoutlimitation, input may include an explicit signal, wherein the pilotexplicitly enters a requested aircraft force and/or flight maneuver. Asa further non-limiting example, pilot input may include an explicitsignal directing a rudder to rotate 3°. As a further non-limitingexample, pilot input may include an implicit signal, wherein flightcontroller 104 detects a torque alteration, flight path deviation, andthe like thereof, wherein aircraft requested force 108 may be obtainedas a function of the torque alteration, flight path deviation, and thelike thereof. In an embodiment, and without limitation, pilot input mayinclude one or more explicit signals to reduce torque, and/or one ormore implicit signals that torque may be reduced due to reduction ofairspeed velocity. In an embodiment, and without limitation, pilot inputmay include one or more local and/or global requested aircraft forces.For example, and without limitation, pilot input may include a localrequested aircraft force that is transmitted by a pilot and/or crewmember. As a further non-limiting example, pilot input may include aglobal requested aircraft force that may be transmitted by air trafficcontrol and/or one or more remote users that are in communication withthe pilot and/or flight controller of eVTOL aircraft. In an embodiment,pilot input may be obtained as a function of a tri-state bus and/ormultiplexor that denotes an explicit pilot input should be transmittedprior to any implicit or global requested aircraft force.

Still referring to FIG. 1 , flight controller 104 is configured togenerate an optimal command mix 112 as a function of requested aircraftforce 108. As used in this disclosure an “optimal command mix” is aplurality of angles, torques, and/or forces that produces an optimizedeffectiveness of one or more flight elements, wherein effectiveness isthe degree to which something is successful and/or efficient. Forexample, and without limitation, optimal command mix 112 may denote thatan angle of 4° for a rudder may be optimal, wherein an angle of 7° maybe inefficient and/or non-optimal. As a further non-limiting example,optimal command mix 112 may denote that a torque of 240 lb. ft. oftorque may be optimal for a propeller. Optimal command mix 112 includesa plurality of commands. As used in this disclosure a “command” is aflight maneuver and/or adjustment to be performed. For example, andwithout limitation, command may denote an adjustment of one or moreflight components, wherein a flight component is described below indetail. Optimal command mix 112 includes a plurality of commands to aplurality of actuators. As used in this disclosure an “actuator” is amotor that may adjust an angle and/or position of one or more flightcomponents, wherein a flight component is described below in detail. Forexample, and without limitation an actuator may adjust a rotor 4° in thehorizontal axis. As a further non, limiting example, an actuator mayadjust a propulsor from a first vertically aligned angle to a secondvertically aligned angle. As a further non-limiting example, an actuatormay comprise a motor and/or motor shaft, wherein actuator commands theshaft to rotate at a specified rotational speed and/or rotationalvelocity. In an embodiment, and without limitation, actuator may besecured to a flight component, wherein a flight component is a portionof an aircraft that can be moved or adjusted to affect one or moreflight elements as described below, in reference to FIG. 3 . As used inthis disclosure, “secured” means that at least a portion of a device,component, or circuit is connected to at least a portion of the aircraftvia a mechanical coupling and/or attachment and/or fastening componentand/or mechanism. Securement may be accomplished, without limitation, bybolting, riveting, welding, press fitting, and the like thereof. Forexample, and without limitation a solid and/or round head rivet may beused to attach a flight component to an actuator. As a furthernon-limiting example, a blind and/or pop rivet may be used to attach aflight component to an actuator. As a further non-limiting example, anoxy-acetylene weld and/or electric arc weld may be used to attach aflight component to an actuator. As a further non-limiting example, ashielded metal arc weld and/or gas metal arc weld may be used to attacha flight component to an actuator. As a further non-limiting example, acomposite press-fit insert may be used to attach a flight component toan actuator. As a non-limiting example, flight component may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight componentmay include a rudder to control yaw of an aircraft. In an embodiment,actuator may maneuver and/or orient a flight component. As anon-limiting example, optimal command mix 112 may command an actuatorsecured to a flight component secured at a first vertical axis, whereinthe first vertical axis may include a 3° inward and/or 1.4° forward, tomaneuver and/or shift+/−15° in the horizontal and/or longitudinal axis.In an embodiment, and without limitation, actuator may include landinggear. Landing gear may be used for take-off and/or landing/Landing gearmay be used to contact ground while aircraft 300 is not in flight.Exemplary landing gear is disclosed in detail in U.S. patent applicationSer. No. 17/196,719 entitled “SYSTEM FOR ROLLING LANDING GEAR” by R.Griffin et al., which is incorporated in its entirety herein byreference.

Still referring to FIG. 1 , flight controller 104 is configured toreceive an ideal actuator model 116, the ideal actuator model 116including at least a performance parameter 120. As used in thisdisclosure an “ideal actuator model” is a set of data corresponding to avirtual actuator's torque output. Ideal actuator model 116 may be acomputer program or computer application that represents actuator torqueperformance given a certain set of conditions. This set of conditionsincludes performance parameter 120. As used in this disclosure“performance parameter” is a parameter denoting an element that effectsan aircraft function. For example, and without limitation, performanceparameter 120 may be environmental such as air density, air speed, trueairspeed, relative airspeed, temperature, humidity level, and weatherconditions, among others. Performance parameter 120 may include actuatorparameters that define an actuator physical characteristics and/orspecifications such as material properties, electrical characteristics,actuator type, weight, geometry, speed, and revolutions per minute(rpm), among others. Performance parameter 120 may include velocityand/or speed in a plurality of ranges and direction such as verticalspeed, horizontal speed, changes in angle or rates of change in angleslike pitch rate, roll rate, yaw rate, or a combination thereof, amongothers.

Still referring to FIG. 1 , flight controller 104 generates optimalcommand mix 112 as a function of producing a model datum 124. As used inthis disclosure a “model datum” is an element of data that represents anideal actuator output form an ideal actuator model. One of ordinaryskill in the art, after reviewing the entirety of this disclosure, wouldappreciate that model datum 124 is the actuator output of an idealvirtual actual data from a perfect actuator given performance parameter120 of a plurality of performance parameters. For example, in anonlimiting embodiment, ideal actuator model 116 may include performanceparameter 120 including air density, actuator type, electrical input,and rpm. Model datum 124 may be produced by flight controller 104 torepresent what a perfect (ideal) actuator would output as torque giventhose performance parameters 120. In an embodiment, and withoutlimitation, flight controller may include a model datum threshold,wherein model datum threshold may include a range of acceptable actuatorvalues associated with model datum 124. Model datum threshold may be aminimum and maximum actuator value associated with model datum. Flightcontroller 104 may be configured to detect if output actuator datum isoutside model datum threshold, which may then trigger detection ofdatums consistent with this disclosure. Model datum may additionally oralternatively include any model datum used as a model troque datumand/or model datum as described in U.S. Nonprovisional application Ser.No. 17/186,079, filed on Feb. 26, 2021, and entitled “METHODS AND SYSTEMFOR ESTIMATING PERCENTAGE TORQUE PRODUCED BY A PROPULSOR CONFIGURED FORUSE IN AN ELECTRIC AIRCRAFT,” the entirety of which is incorporatedherein by reference.

In an embodiment, and still referring to FIG. 1 , flight controller 104may utilize stored data to produce model datum 124. Stored data may bepast actuator outputs related to performance parameters 120 desired forthe instant model in an embodiment of the present invention. Stored datamay be input by a user, pilot, support personnel, or another. Storeddata may include algorithms and machine-learning processes that mayproduce model datum 124 considering at least a performance parameter120. The algorithms and machine-learning processes may be any algorithmor machine-learning processes as described herein. Training data may becolumns, matrices, rows, blocks, spreadsheets, books, or other suitabledatastores or structures that contain correlations between past torqueoutputs to performance parameters. Training data may be any trainingdata as described below. Training data may be past measurements detectedby any sensors described herein or another sensor or suite of sensors incombination. Training data may be detected by onboard or offboardinstrumentation designed to detect output torque and performanceparameters as described herein. Training data may be uploaded,downloaded, and/or retrieved from a server prior to flight. Trainingdata may be generated by a computing device that may simulate actuatoroutputs and correlated performance parameters suitable for use by theflight controller 104 in an embodiment of the present invention. Flightcontroller 104 and/or another computing device as described in thisdisclosure may train one or more machine-learning models using thetraining data as described in this disclosure. Training one or moremachine-learning models consistent with the training one or more machinelearning modules as described in this disclosure.

In an embodiment, and without limitation, flight controller 104 mayproduce model datum 124 as a function of determining an effectivityelement. As used in this disclosure an “effectivity element” is ameasurable value representing an effectiveness and/or efficiency of anactuator in performing a flight maneuver and/or action. For example, andwithout limitation effectivity element may denote one or moreefficiencies represented as a percentage, ratio, true/false, yes/no, andthe like thereof. As a further non-limiting example, effectivity elementmay denote that an actuator is 58% effective at successfully performingan action and/or flight maneuver. In an embodiment, and withoutlimitation, flight controller 104 may determine effectivity element as afunction of identifying an effectiveness of flight component. As used inthis disclosure an “effectiveness” is a measurable value representing adegree to which a flight component is successful in producing a flightmaneuver. For example, and without limitation, a flight componentcomprising a propulsor may have an effectiveness of 88% for producing alift, wherein a rudder may have an effectiveness of 12% for producingthe lift. As a further non-limiting example, a flight componentcomprising an aileron may have an effectiveness of 94% for turning anaircraft, wherein a landing gear may have an effectiveness of 9% forturning an aircraft. In an embodiment and without limitation, flightcontroller 104 may effectivity element may include an actuatorpercentage datum. An “actuator percentage datum”, for the purposes ofthis disclosure, is an element of data representing the actual actuatorforce produced by an actuator compared to the modeled actuator forceoutput of the same ideal actuator given the same performance parameters.For example, in a nonlimiting embodiment, flight controller 104 maygenerate actuator percentage datum by dividing output actuator forcedatum by model datum, wherein output actuator force datum may bedetected by a sensor and model datum generated from receiving idealactuator model 116. Performance parameter 120 may replicate theconditions that actuator is operating under. For example, in anonlimiting embodiment, performance parameter 120 may include airdensity, temperature, humidity, propulsor type and electrical input thatmatch exactly values the actual eVTOL aircraft is operating under, andtherefore model datum 124 would represent an ideal actuator in thoseconditions. Actuator percentage datum, in other words, may represent thetorque output of an actual propulsor versus the same propulsor in anideal world, giving way to a percentage of ideal actuator force.Actuator percentage datum may be represented as a fraction, percentage,decimal, or other mathematical representation of part of a whole. One ofordinary skill in the art, after reviewing the entirety of thisdisclosure would appreciate that there are virtually limitless visual,auditory, haptic or other types of representations that actuatorpercentage datum may take.

In an embodiment, and still referring to FIG. 1 , flight controller 104may generate optimal command mix 112 as a function of dynamicallyvarying the plurality of actuators as a function of requested aircraftforce 108. As used in this disclosure “dynamically varying” is a processof altering and/or adjusting an actuator as a function of the requestedaircraft force. For example, and without limitation, dynamically varyingmay include identifying a first model datum as a function of a firstrequested aircraft force and subsequently identifying a second modeldatum as a function of a second requested aircraft force. In anembodiment, and without limitation, flight controller 104 maydynamically vary actuator such that a maximal effectiveness isdetermined. As used in this disclosure a “maximal effectiveness” is amaximum measurable value associated with an effectiveness and/orefficiency. For example, and without limitation, a propulsor may exert amaximal effectiveness for producing lift as a function of an actuatororienting the propulsor at 3° inward. In an embodiment, and withoutlimitation, flight controller 104 may dynamically vary actuator withrequested aircraft force 108 as a function of producing an effectivitysimulation. As used in this disclosure an “effectivity simulation” is animitation of eVTOL aircraft, actuator, and/or flight component anaircraft. For example, and without limitation, effectivity simulationmay denote at least a flight element of VTOL aircraft, wherein a flightelement is an element of datum denoting a relative status of aircraftdescribed below in detail, in reference to FIG. 3 . In an embodiment,and without limitation, flight element may denote one or more torques,thrusts, airspeed velocities, forces, altitudes, groundspeed velocities,directions during flight, directions facing, forces, orientations, andthe like thereof. In an embodiment, and without limitation, flightcontroller 104 may produce effectivity simulation denoting one or moreadjustments to an effectiveness as a function of a requested aircraftforce 108. For example, and without limitation, flight controller 104may produce effectivity simulation denoting one or more adjustments toan effectiveness as a function of an adjusted and/or shifted flightcomponent and/or actuator during flight. As a further non-limitingexample, flight controller 104 may produce effectivity simulationdenoting one or more modifications to an efficiency as a function of achanging and/or altered actuator orientation. As a further non-limitingexample, flight controller 104 may produce effectivity simulationdenoting one or more modifications to an efficiency as a function of achanging and/or altered flight component. In an embodiment, and withoutlimitation, flight controller 104 may be configured to includeoperational data of flight component for a plurality of simulatedconditions. As used in this disclosure “operational data” is informationdenoting one or more operational functions of a flight component. Forexample, and without limitation, operational data may denote one or morerotational speeds, torques, forces, rpms, and the like thereof. Forexample, and without limitation, operational data may denote that apropeller is rotating at a speed of 800 rpms. As a further non-limitingexample, operational data may denote that an aileron is angled at 3.3°upward. In an embodiment, and without limitation, operational data maydenote one or more voltage levels, electromotive force, current levels,temperature, current speed of rotation, and the like thereof. In anotherembodiment, operational data may denote one or more electricalparameters of a flight component such as a voltage, current, and/orohmic resistance of flight component. As used in this disclosure a“simulated condition” is a condition and/or environment that is to besimulated for flight condition. For example, and without limitation,simulated conditions may include an environmental condition of a windforce and/or precipitation. As a further non-limiting example, simulatedconditions may include one or more alterations and/or modifications ofoperational datum.

Still referring to FIG. 1 , flight controller 104 may generate optimalcommand mix 112 as a function of requested aircraft force 108 and modeldatum 124. In an embodiment, and without limitation, flight controller104 may generate optimal command mix 112 as a function of training anoptimal machine-learning model. As used in this disclosure a “optimalmachine-learning model” is a machine-learning model to generate anoptimal command mix output given a requested aircraft force and/or modeldatum as an input; this is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language. Optimalmachine-learning model may include one or more optimal machine-learningprocesses such as supervised, unsupervised, or reinforcementmachine-learning processes that flight controller 104 and/or a remotedevice may or may not use in the generation of optimal command mix 112.As used in this disclosure “remote device” is an external device toflight controller 104. Additionally or alternatively, optimalmachine-learning model may include one or more optimal machine-learningprocesses that a field-programmable gate array (FPGA) may or may not usein the generation of command. Optimal machine-learning process mayinclude, without limitation machine learning processes such as simplelinear regression, multiple linear regression, polynomial regression,support vector regression, ridge regression, lasso regression,elasticnet regression, decision tree regression, random forestregression, logistic regression, logistic classification, K-nearestneighbors, support vector machines, kernel support vector machines,naïve bayes, decision tree classification, random forest classification,K-means clustering, hierarchical clustering, dimensionality reduction,principal component analysis, linear discriminant analysis, kernelprincipal component analysis, Q-learning, State Action Reward StateAction (SARSA), Deep-Q network, Markov decision processes, DeepDeterministic Policy Gradient (DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 1 , optimal machinelearning model may be trained as a function of an optimal training set,wherein optimal training set may correlate a plurality of requestedaircraft forces to a plurality of model data to an optimal command mix.Optimal training data may be received as a function of user-enteredvaluations of requested aircraft forces, model data, and/or optimalcommand mixes. Flight controller 104 may receive optimal training databy receiving correlations of requested aircraft forces and/or model datato optimal command mixes that were previously received and/or determinedduring a previous iteration of generation of optimal command mix 112.Optimal training data may be received by one or more remote devicesand/or FPGAs that at least correlate a requested aircraft force and/ormodel datum to an optimal command mix. Optimal training data may bereceived in the form of one or more user-entered correlations of arequested aircraft force and/or model datum to an optimal command mix.Additionally or alternatively, flight controller 104 may receive optimalmachine-learning model from a remote device and/or FPGA that utilizesone or more optimal machine learning processes, wherein a remote deviceand an FPGA is described above in detail. For example, and withoutlimitation, a remote device may include a computing device, externaldevice, processor, FPGA, microprocessor and the like thereof. Remotedevice and/or FPGA may perform the optimal machine-learning processusing optimal training data to generate optimal command mix 112 andtransmit the output to flight controller 104. Remote device and/or FPGAmay transmit a signal, bit, datum, or parameter to flight controller 104that at least relates to command. Additionally or alternatively, theremote device and/or FPGA may provide an updated machine-learning model.For example, and without limitation, an updated machine-learning modelmay be comprised of a firmware update, a software update, an optimalmachine-learning process correction, and the like thereof. As anon-limiting example a software update may incorporate a new requestedaircraft force that relates to a modified model datum. Additionally oralternatively, the updated machine learning model may be transmitted tothe remote device and/or FPGA, wherein the remote device and/or FPGA mayreplace the optimal machine-learning model with the updatedmachine-learning model and generate the optimal command mix as afunction of the model datum, requested aircraft force and/or optimalcommand mix using the updated machine-learning model. The updatedmachine-learning model may be transmitted by the remote device and/orFPGA and received by flight controller 104 as a software update,firmware update, or corrected optimal machine-learning model. Forexample, and without limitation optimal machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process. Machine-learning model may be transmitted,without limitation, in the form of a software update, a firmware update,and/or a bitstream reconfiguring an FPGA or similar device;machine-learning model may be transmitted in the form of coefficients,weights, and/or other parameters that have been tuned as part of amachine-learning process as described in further detail below.

Still referring to FIG. 1 , optimal machine-learning model may include aclassifier. A “classifier,” as used in this disclosure is amachine-learning model, such as a mathematical model, neural net, orprogram generated by a machine learning algorithm known as a“classification algorithm,” as described in further detail below, thatsorts inputs into categories or bins of data, outputting the categoriesor bins of data and/or labels associated therewith. A classifier may beconfigured to output at least a datum that labels or otherwiseidentifies a set of data that are clustered together, found to be closeunder a distance metric as described below, or the like. Flightcontroller 104 and/or another device may generate a classifier using aclassification algorithm, defined as a processes whereby a flightcontroller 104 derives a classifier from training data. Classificationmay be performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers.

Still referring to FIG. 1 , flight controller 104 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(A/B) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Flightcontroller 104 may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Flight controller 104 may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1 , flight controller 104 may beconfigured to generate a classifier using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample-features resemble training data toclassify input data to one or more clusters and/or categories offeatures as represented in training data; this may be performed byrepresenting both training data and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute/as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)α_(i) ²)}, whereα_(i), is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values.

Still referring to FIG. 1 , flight controller 104 may generate optimalcommand mix 112 as a function of generating an objection function toscore and weight factors to achieve an optimal command mix for eachgrouping of requested aircraft forces and/or model data. In someembodiments, groupings may be scored in a matrix for optimization, wherecolumns represent predictive prevalence value and rows represent poolingthresholds potentially paired therewith; each cell of such a matrix mayrepresent a score of a grouping of the corresponding optimal commandmix.

With continued reference to FIG. 1 , flight controller 104 may generateoptimal command mix 112 as a function of optimizing the objectivefunction by performing a greedy algorithm process. A “greedy algorithm”is defined as an algorithm that selects locally optimal choices, whichmay or may not generate a globally optimal solution. For instance,flight controller 104 may select groupings so that scores associatedtherewith are the best score for each predictive prevalence value and/orfor each pooling threshold. In such an example, optimization maygenerate optimal command mix 112 such that each actuator position and/ororientation includes the highest score possible.

Still referring to FIG. 1 , objective function may be formulated as alinear objective function. Which flight controller 104 may solve using alinear program such as without limitation a mixed-integer program. A“linear program,” as used in this disclosure, is a program thatoptimizes a linear objective function, given at least a constraint. Forinstance, and without limitation, objective function may seek tomaximize a total score Σ_(rER) Σ_(sES) c_(rs)x_(rs), where R is the setof all predictive prevalence values r, S is a set of all poolingthresholds s, c_(rs) is a score of a grouping of a given predictiveprevalence value with a given pooling threshold, and x_(rs) is 1 if apredictive prevalence value r is grouped with a pooling threshold s, and0 otherwise. Continuing the example, constraints may specify that eachpredictive prevalence value is assigned to only one pooling threshold,and each pooling threshold is assigned only one predictive prevalencevalue. Sets of predictive prevalence values may be optimized for amaximum score combination of all generated predictive prevalence values.In various embodiments, flight controller 104 may determine combinationof predictive prevalence values that maximizes a total score subject toa constraint that all predictive prevalence values are paired to exactlyone pooling threshold. A mathematical solver may be implemented to solvefor the set of feasible groupings that maximizes the sum of scoresacross all groupings; mathematical solver may implemented on flightcontroller 104 and/or another remote device, and/or may be implementedon third-party solver.

With continued reference to FIG. 1 , optimizing objective function mayinclude minimizing a loss function, where a “loss function” is anexpression an output of which an optimization algorithm minimizes togenerate an optimal result. As a non-limiting example, flight controller104 may assign variables relating to a set of parameters, which maycorrespond to score components as described above, calculate an outputof mathematical expression using the variables, and select a groupingthat produces an output having the lowest size, according to a givendefinition of “size,” of the set of outputs representing each predictiveprevalence value; size may, for instance, included absolute value,numerical size, or the like. Selection of different loss functions mayresult in identification of different potential groupings as generatingminimal outputs. Objectives represented in an objective function and/orloss function may include maximizing the requested aircraft force and/ormaximizing the optimal command mix.

Still referring to FIG. 1 , flight controller 104 may generate optimalcommand mix 112 as a function of receiving a sensor input. As used inthis disclosure a “sensor input” is one or more inputs denoting one ormore current distances, angles, orientations, speeds, velocities,forces, visual representations and the like thereof associated to anaircraft. For example, and without limitation, sensor input may denotethat eVTOL aircraft is 500 m above ground. As a further non-limitingexample, sensor input may denote that eVTOL aircraft is angled at 3°eastward, wherein eVTOL aircraft is traveling at a velocity of 910 km/h.As a further non-limiting example sensor input may denote that eVTOLaircraft comprises a 3° lift angle and/or a 7° angle of attack.Additionally or alternatively, sensor input may denote one or moreforces representing an eVTOL degree of freedom of movement, such as butnot limited to forces in the forward/back, side/side, and up/downdirections and moments about the longitudinal (roll) axis, thetransverse (pitch) axis, and/or the vertical (yaw) axis. Additionally oralternatively, sensor input may denote an inertial measurement. As usedin this disclosure an “inertial measurement” is an element of datumdenoting one or more forces, angular rates, and/or orientations. Forexample, and without limitation, inertial measurement may include ameasurement of 5 m/s² for an aircraft's acceleration in a northeasterndirection. In an embodiment, inertial measurement may include generatinga moving map display. As used in this disclosure a “moving map display”is a digital map archive representing one or more position outputs. Forexample, and without limitation, moving map display may identify one ormore movements, orientations, and/or velocities of aircraft over adigital map. In an embodiment, and without limitation, inertialmeasurement may be determined as a function of magnetic sensors ormagnetometers such as Hall effect sensors, compasses such as solid-statecompasses, or the like; one or more magnetometers may include aplurality of magnetometers, such as three or more magnetometerspositioned to span three dimensions of possible orientation, so that anydirection and magnitude of change in magnetic field in three dimensionsmay be detected and measured in three dimensions, possibly formeasurement of the aircraft's orientation to the Earth's true North ordetection of magnetic anomalies. In another embodiment, inertialmeasurement may be determined as a function of a MEMS sensor, inertialmeasurement unit (IMU), an accelerometer, wherein one or moreaccelerometers may include a plurality of accelerometers, such as threeor more accelerometers positioned to span three dimensions of possibleacceleration, so that any direction and magnitude of acceleration inthree dimensions may be detected and measured in three dimensions, andthe like thereof. In another embodiment, and without limitation,inertial measurement may be determined as a function of one or moregyroscopes; one or more gyroscopes may include a plurality ofgyroscopes, such as three or more gyroscopes positioned to span threedimensions of possible acceleration, so that any direction and magnitudeof change in angular position in three dimensions may be detected andmeasured in three dimensions. Additionally or alternatively, sensorinput may denote one or more images and/or visual representations suchas snapshots, pictures, videos, and the like thereof.

Still referring to FIG. 1 , flight controller 104 may receive sensorinput as a function of obtaining an aircraft status. As used in thisdisclosure an “aircraft status” is status of aircraft for both criticaland non-critical functions. For example, and without limitation,aircraft status may denote one or more functions and/or operations thatare operating within a safe range. As a further non-limiting example,aircraft status may denote one or more functions and/or operations thatare operating within a hazardous range. In an embodiment, and withoutlimitation, aircraft status may denote a wing tilt angle. As used inthis disclosure a “wing tilt angle” is an angle that extends from thelongitudinal axis to the vertical yaw axis. In an embodiment, andwithout limitation, wing tilt angle may denote a wing pitch and/or pitchangle. Additionally or alternatively, aircraft status may denote a rotortilt angle. As used in this disclosure a “rotor tilt angle” is an anglethat extends from the longitudinal axis to a secondary axis, such as butnot limited to the vertical yaw axis and/or the pitch axis. For example,and without limitation, rotor tilt angle may include a nominal angle. Asused in this disclosure a “nominal angle” is an angle of the rotor in ahorizontal axis. For example, and without limitation, a nominal anglemay include a 3° angle tilted forward and/or a 3° angle tilted backward.Additionally or alternatively, rotor tilt angle may include a cantedangle. As used in this disclosure a “canted angle” is an angle of thepropulsor in longitudinal direction. For example, and withoutlimitation, a nominal angle may include a 5.5° angle tilted inwardand/or a 5.5° angle tilted outward. In an embodiment, and withoutlimitation, rotor tilt angle may be comprised of a nominal angle and/ora canted angle. For example, and without limitation, rotor tilt anglemay be an angle of 3.4° inward and/or 5.2° forward. As a furthernon-limiting example, a fixed angle may be an angle of 3 inward and/or0.6° forward.

Still referring to FIG. 1 , sensor input may be received as a functionof a sensor. As used in this disclosure a “sensor” is a device, module,and/or subsystem, utilizing any hardware, software, and/or anycombination thereof to detect events and/or changes in the instantenvironment and transmit the information. Sensor may be attached via amechanically and/or communicatively coupled to eVTOL aircraft. As usedherein, “communicatively connecting” is a process whereby one device,component, or circuit is able to receive data from and/or transmit datato another device, component, or circuit. A communicative connection maybe achieved through wired or wireless electronic communication, eitherdirectly or by way of one or more intervening devices or components.Further, communicative connecting can include electrically coupling atleast an output of one device, component, or circuit to at least aninput of another device, component, or circuit. For example, via a busor other facility for intercommunication between elements of a computingdevice as described in this disclosure. Communicative connecting mayalso include indirect connections via wireless connection, radiocommunication, low power wide area network, optical communication,magnetic, capacitive, or optical coupling, or the like. For example, andwithout limitation, sensor may include a potentiometric sensor,inductive sensor, capacitive sensor, piezoelectric sensor, strain gaugesensor, variable reluctance sensor, and the like thereof. Sensor mayinclude one or more environmental sensors, which may function to senseparameters of the environment surrounding the aircraft. An environmentalsensor may include without limitation one or more sensors used to detectambient temperature, barometric pressure, and/or air velocity, one ormore motion sensors which may include without limitation gyroscopes,accelerometers, inertial measurement unit (IMU), and/or magneticsensors, one or more humidity sensors, one or more oxygen sensors, orthe like. Additionally or alternatively, sensor may include a geospatialsensor. Sensor may be located inside aircraft; and/or be included inand/or attached to at least a portion of the aircraft. Sensor mayinclude one or more proximity sensors, displacement sensors, vibrationsensors, and the like thereof. Sensor may be used to monitor the statusof aircraft for both critical and non-critical functions. Sensor may beincorporated into vehicle or aircraft or be remote. Sensor may becommunicatively connected to an energy source and/or motor, wherein anenergy source and motor are described in detail below, in reference toFIG. 2 , and wherein sensor detects one or more conditions of the energysource and/or motor.

Still referring to FIG. 1 , flight controller 104 produces a controlsignal 128 as a function of optimal command mix 112. As used in thisdisclosure a “control signal” is a direction and/or guidance directingan actuator and/or a flight component to perform an action and/ormotion. In an embodiment, and without limitation, control signal 128 mayinclude a command to alter and/or shift about an axis. For example, andwithout limitation, control signal 128 may include a command to rotate arudder 3° about a vertical axis. In another embodiment, and withoutlimitation, control signal 128 may include a command to reverse a firsttorque magnitude and/or direction. In another embodiment, and withoutlimitation, control signal 128 may include one or more commands todirect a flight component and/or actuator to alter a heading, speed,altitude, departure angle, approach angle, route paths, and the likethereof.

Still referring to FIG. 1 , flight controller 104 may produce controlsignal 128 as a function of generating an autonomous function. As usedin this disclosure an “autonomous function” is a mode and/or function offlight controller 104 that controls an actuator, flight component,and/or eVTOL aircraft automatically. For example, and withoutlimitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function ofmaneuvering and/or adjusting an actuator, flight component, and the likethereof. In an embodiment, autonomous function may include one or moremodes of autonomy such as, but not limited to, autonomous mode,semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 104 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 104 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 1 , producing controlsignal 128 may comprise transmitting a notification to a pilot. As usedin this disclosure a “notification” is a signal and/or form ofcommunication that relays a message regarding and/or representingoptimal command mix 112. In an embodiment, and without limitation,notification may be transmitted as a function of a notification unit. Asused in this disclosure a “notification unit” is a component capable ofproducing and/or emitting a notification and/or signal to a pilot. In anembodiment, and without limitation, notification unit may include agraphical user interface (GUI). For the purposes of this disclosure, a“graphical user interface” is a device configured to present data orinformation in a visual manner to a pilot, computer, camera orcombination thereof. Notification unit may be configured to displayinformation regarding eVTOL aircraft, optimal command mix, actuator,and/or flight component. Notification unit may be configured to displayinformation regarding a failure of a flight component and/or a failureof an energy source. Notification unit may prompt a pilot to input apilot signal as a function of a required interaction and/or response.Notification unit may be configured to receive haptic, audio, visual,gesture, passkey, or other type of interaction from the pilot.Notification unit may perform one or more functions in response to theinteraction from the pilot. In non-limiting examples, and withoutlimitation, notification unit may transmit a pilot input to flightcontroller 104 when an affirmative interaction is received from thepilot, the signal indicating to transmit one or more signals to othercomponents communicatively connected thereto, such as flight componentand/or actuator.

Now referring to FIG. 2 , an eVTOL aircraft 200 is illustrated. eVTOLaircraft 200 may include any eVTOL aircraft as described above, inreference to FIG. 1 . eVTOL aircraft may include a fuselage 204. As usedin this disclosure a “fuselage” is the main body of an aircraft, or inother words, the entirety of the aircraft except for the cockpit, nose,wings, empennage, nacelles, any and all control surfaces, and generallycontains an aircraft's payload. Fuselage 204 may comprise structuralelements that physically support the shape and structure of an aircraft.Structural elements may take a plurality of forms, alone or incombination with other types. Structural elements may vary depending onthe construction type of aircraft and specifically, the fuselage.Fuselage 204 may comprise a truss structure. A truss structure is oftenused with a lightweight aircraft and comprises welded steel tubetrusses. A truss, as used herein, is an assembly of beams that create arigid structure, often in combinations of triangles to createthree-dimensional shapes. A truss structure may alternatively comprisewood construction in place of steel tubes, or a combination thereof. Inembodiments, structural elements may comprise steel tubes and/or woodbeams. In an embodiment, and without limitation, structural elements mayinclude an aircraft skin. Aircraft skin may be layered over the bodyshape constructed by trusses. Aircraft skin may comprise a plurality ofmaterials such as plywood sheets, aluminum, fiberglass, and/or carbonfiber, the latter of which will be addressed in greater detail later inthis paper.

In embodiments, fuselage 204 may comprise geodesic construction.Geodesic structural elements may include stringers wound about formers(which may be alternatively called station frames) in opposing spiraldirections. A stringer, as used herein, is a general structural elementthat comprises a long, thin, and rigid strip of metal or wood that ismechanically coupled to and spans the distance from, station frame tostation frame to create an internal skeleton on which to mechanicallycouple aircraft skin. A former (or station frame) can include a rigidstructural element that is disposed along the length of the interior offuselage 204 orthogonal to the longitudinal (nose to tail) axis of theaircraft and forms the general shape of fuselage 204. A former maycomprise differing cross-sectional shapes at differing locations alongfuselage 204, as the former is the structural element that informs theoverall shape of a fuselage 204 curvature. In embodiments, aircraft skincan be anchored to formers and strings such that the outer mold line ofthe volume encapsulated by the formers and stringers comprises the sameshape as eVTOL aircraft 200 when installed. In other words, former(s)may form a fuselage's ribs, and the stringers may form the interstitialsbetween such ribs. The spiral orientation of stringers about formersprovides uniform robustness at any point on an aircraft fuselage suchthat if a portion sustains damage, another portion may remain largelyunaffected. Aircraft skin would be mechanically coupled to underlyingstringers and formers and may interact with a fluid, such as air, togenerate lift and perform maneuvers.

In an embodiment, and still referring to FIG. 2 , fuselage 204 maycomprise monocoque construction. Monocoque construction may include aprimary structure that forms a shell (or skin in an aircraft's case) andsupports physical loads. Monocoque fuselages are fuselages in which theaircraft skin or shell is also the primary structure. In monocoqueconstruction aircraft skin would support tensile and compressive loadswithin itself and true monocoque aircraft can be further characterizedby the absence of internal structural elements. Aircraft skin in thisconstruction method is rigid and can sustain its shape with nostructural assistance form underlying skeleton-like elements. Monocoquefuselage may comprise aircraft skin made from plywood layered in varyinggrain directions, epoxy-impregnated fiberglass, carbon fiber, or anycombination thereof.

According to embodiments, fuselage 204 may include a semi-monocoqueconstruction. Semi-monocoque construction, as used herein, is a partialmonocoque construction, wherein a monocoque construction is describeabove detail. In semi-monocoque construction, fuselage 204 may derivesome structural support from stressed aircraft skin and some structuralsupport from underlying frame structure made of structural elements.Formers or station frames can be seen running transverse to the longaxis of fuselage 204 with circular cutouts which are generally used inreal-world manufacturing for weight savings and for the routing ofelectrical harnesses and other modern on-board systems. In asemi-monocoque construction, stringers are the thin, long strips ofmaterial that run parallel to fuselage's long axis. Stringers may bemechanically coupled to formers permanently, such as with rivets.Aircraft skin may be mechanically coupled to stringers and formerspermanently, such as by rivets as well. A person of ordinary skill inthe art will appreciate that there are numerous methods for mechanicalfastening of the aforementioned components like crews, nails, dowels,pins, anchors, adhesives like glue or epoxy, or bolts and nuts, to namea few. A subset of fuselage under the umbrella of semi-monocoqueconstruction is unibody vehicles. Unibody, which is short for “unitizedbody” or alternatively “unitary construction”, vehicles arecharacterized by a construction in which the body, floor plan, andchassis form a single structure. In the aircraft world, unibody wouldcomprise the internal structural elements like formers and stringers areconstructed in one piece, integral to the aircraft skin as well as anyfloor construction like a deck.

Still referring to FIG. 2 , stringers and formers which account for thebulk of any aircraft structure excluding monocoque construction can bearranged in a plurality of orientations depending on aircraft operationand materials. Stringers may be arranged to carry axial (tensile orcompressive), shear, bending or torsion forces throughout their overallstructure. Due to their coupling to aircraft skin, aerodynamic forcesexerted on aircraft skin will be transferred to stringers. The locationof said stringers greatly informs the type of forces and loads appliedto each and every stringer, all of which may be handled by materialselection, cross-sectional area, and mechanical coupling methods of eachmember. The same assessment may be made for formers. In general, formersare significantly larger in cross-sectional area and thickness,depending on location, than stringers. Both stringers and formers maycomprise aluminum, aluminum alloys, graphite epoxy composite, steelalloys, titanium, or an undisclosed material alone or in combination.

In an embodiment, and still referring to FIG. 2 , stressed skin, whenused in semi-monocoque construction is the concept where the skin of anaircraft bears partial, yet significant, load in the overall structuralhierarchy. In other words, the internal structure, whether it be a frameof welded tubes, formers and stringers, or some combination, is notsufficiently strong enough by design to bear all loads. The concept ofstressed skin is applied in monocoque and semi-monocoque constructionmethods of fuselage 204. Monocoque comprises only structural skin, andin that sense, aircraft skin undergoes stress by applied aerodynamicfluids imparted by the fluid. Stress as used in continuum mechanics canbe described in pound-force per square inch (lbf/in²) or Pascals (Pa).In semi-monocoque construction stressed skin bears part of theaerodynamic loads and additionally imparts force on the underlyingstructure of stringers and formers.

Still referring to FIG. 2 , it should be noted that an illustrativeembodiment is presented only, and this disclosure in no way limits theform or construction of eVTOL aircraft 200. In embodiments, fuselage 204may be configurable based on the needs of the eVTOL per specific missionor objective. The general arrangement of components, structuralelements, and hardware associated with storing and/or moving a payloadmay be added or removed from fuselage 204 as needed, whether it isstowed manually, automatedly, or removed by personnel altogether.Fuselage 204 may be configurable for a plurality of storage options.Bulkheads and dividers may be installed and uninstalled as needed, aswell as longitudinal dividers where necessary. Bulkheads and dividersmay be installed using integrated slots and hooks, tabs, boss andchannel, or hardware like bolts, nuts, screws, nails, clips, pins,and/or dowels, to name a few. Fuselage 204 may also be configurable toaccept certain specific cargo containers, or a receptable that can, inturn, accept certain cargo containers.

Still referring to FIG. 2 , eVTOL aircraft 200 may include a pluralityof laterally extending elements 208 attached to fuselage 204. As used inthis disclosure a “laterally extending element” is an element thatprojects essentially horizontally from fuselage, including an outrigger,a spar, and/or a fixed wing that extends from fuselage. Wings may bestructures which include airfoils configured to create a pressuredifferential resulting in lift. Wings may generally dispose on the leftand right sides of the aircraft symmetrically, at a point between noseand empennage. Wings may comprise a plurality of geometries in planformview, swept swing, tapered, variable wing, triangular, oblong,elliptical, square, among others. A wing's cross section may geometrycomprises an airfoil. An “airfoil” as used in this disclosure is a shapespecifically designed such that a fluid flowing above and below it exertdiffering levels of pressure against the top and bottom surface. Inembodiments, the bottom surface of an aircraft can be configured togenerate a greater pressure than does the top, resulting in lift. In anembodiment, and without limitation, wing may include a leading edge. Asused in this disclosure a “leading edge” is a foremost edge of anairfoil that first intersects with the external medium. For example, andwithout limitation, leading edge may include one or more edges that maycomprise one or more characteristics such as sweep, radius and/orstagnation point, droop, thermal effects, and the like thereof. In anembodiment, and without limitation, wing may include a trailing edge. Asused in this disclosure a “trailing edge” is a rear edge of an airfoil.In an embodiment, and without limitation, trailing edge may include anedge capable of controlling the direction of the departing medium fromthe wing, such that a controlling force is exerted on the aircraft.Laterally extending element 208 may comprise differing and/or similarcross-sectional geometries over its cord length or the length from wingtip to where wing meets the aircraft's body. One or more wings may besymmetrical about the aircraft's longitudinal plane, which comprises thelongitudinal or roll axis reaching down the center of the aircraftthrough the nose and empennage, and the plane's yaw axis. Laterallyextending element may comprise controls surfaces configured to becommanded by a pilot or pilots to change a wing's geometry and thereforeits interaction with a fluid medium, like air. Control surfaces maycomprise flaps, ailerons, tabs, spoilers, and slats, among others. Thecontrol surfaces may dispose on the wings in a plurality of locationsand arrangements and in embodiments may be disposed at the leading andtrailing edges of the wings, and may be configured to deflect up, down,forward, aft, or a combination thereof. An aircraft, including adual-mode aircraft may comprise a combination of control surfaces toperform maneuvers while flying or on ground.

Still referring to FIG. 2 , eVTOL aircraft 200 may include a pluralityof lift components 212 attached to the plurality of extending elements208. As used in this disclosure a “lift component” is a component and/ordevice used to propel a craft upward by exerting downward force on afluid medium, which may include a gaseous medium such as air or a liquidmedium such as water. Lift component 212 may include any device orcomponent that consumes electrical power on demand to propel an electricaircraft in a direction or other vehicle while on ground or in-flight.For example, and without limitation, lift component 212 may include arotor, propeller, paddle wheel and the like thereof, wherein a rotor isa component that produces torque along a longitudinal axis, and apropeller produces torquer along a vertical axis. In an embodiment, liftcomponent 212 may include a propulsor. In an embodiment, when apropulsor twists and pulls air behind it, it will, at the same time,push an aircraft forward with an equal amount of force. As a furthernon-limiting example, lift component 212 may include a thrust elementwhich may be integrated into the propulsor. The thrust element mayinclude, without limitation, a device using moving or rotating foils,such as one or more rotors, an airscrew or propeller, a set of airscrewsor propellers such as contra-rotating propellers, a moving or flappingwing, or the like. Further, a thrust element, for example, can includewithout limitation a marine propeller or screw, an impeller, a turbine,a pump-jet, a paddle or paddle-based device, or the like. The more airpulled behind an aircraft, the greater the force with which the aircraftis pushed forward.

In an embodiment, and still referring to FIG. 2 , lift component 212 mayinclude a plurality of blades. As used in this disclosure a “blade” is apropeller that converts rotary motion from an engine or other powersource into a swirling slipstream. In an embodiment, blade may convertrotary motion to push the propeller forwards or backwards. In anembodiment lift component 212 may include a rotating power-driven hub,to which are attached several radial airfoil-section blades such thatthe whole assembly rotates about a longitudinal axis. The blades may beconfigured at an angle of attack. In an embodiment, and withoutlimitation, angle of attack may include a fixed angle of attack. As usedin this disclosure an “fixed angle of attack” is fixed angle between thechord line of the blade and the relative wind. As used in thisdisclosure a “fixed angle” is an angle that is secured and/or unmovablefrom the attachment point. For example, and without limitation fixedangle of attack may be 2.8° as a function of a pitch angle of 8.1° and arelative wind angle 5.3°. In another embodiment, and without limitation,angle of attack may include a variable angle of attack. As used in thisdisclosure a “variable angle of attack” is a variable and/or moveableangle between the chord line of the blade and the relative wind. As usedin this disclosure a “variable angle” is an angle that is moveable fromthe attachment point. For example, and without limitation variable angleof attack may be a first angle of 4.7° as a function of a pitch angle of7.1° and a relative wind angle 2.4°, wherein the angle adjusts and/orshifts to a second angle of 2.7° as a function of a pitch angle of 5.1°and a relative wind angle 2.4°. In an embodiment, angle of attack beconfigured to produce a fixed pitch angle. As used in this disclosure a“fixed pitch angle” is a fixed angle between a cord line of a blade andthe rotational velocity direction. For example, and without limitation,fixed pitch angle may include 18°. In another embodiment fixed angle ofattack may be manually variable to a few set positions to adjust one ormore lifts of the aircraft prior to flight. In an embodiment, blades foran aircraft are designed to be fixed to their hub at an angle similar tothe thread on a screw makes an angle to the shaft; this angle may bereferred to as a pitch or pitch angle which will determine the speed ofthe forward movement as the blade rotates.

In an embodiment, and still referring to FIG. 2 , lift component 212 maybe configured to produce a lift. As used in this disclosure a “lift” isa perpendicular force to the oncoming flow direction of fluidsurrounding the surface. For example, and without limitation relativeair speed may be horizontal to eVTOL aircraft 200, wherein the liftforce may be a force exerted in the vertical direction, directing eVTOLaircraft 200 upwards. In an embodiment, and without limitation, liftcomponent 212 may produce lift as a function of applying a torque tolift component. As used in this disclosure a “torque” is a measure offorce that causes an object to rotate about an axis in a direction. Forexample, and without limitation, torque may rotate an aileron and/orrudder to generate a force that may adjust and/or affect altitude,airspeed velocity, groundspeed velocity, direction during flight, and/orthrust. In an embodiment, and without limitation, lift component 212 mayreceive a source of power and/or energy from a power sources may apply atorque on lift component 212 to produce lift. As used in this disclosurea “power source” is a source that that drives and/or controls anycomponent attached to eVTOL aircraft 200. For example, and withoutlimitation power source may include a motor that operates to move one ormore lift components, to drive one or more blades, or the like thereof.A motor may be driven by direct current (DC) electric power and mayinclude, without limitation, brushless DC electric motors, switchedreluctance motors, induction motors, or any combination thereof. A motormay also include electronic speed controllers or other components forregulating motor speed, rotation direction, and/or dynamic braking.

Still referring to FIG. 2 , power source may include an energy source.An energy source may include, for example, a generator, a photovoltaicdevice, a fuel cell such as a hydrogen fuel cell, direct methanol fuelcell, and/or solid oxide fuel cell, an electric energy storage device(e.g. a capacitor, an inductor, and/or a battery). An energy source mayalso include a battery cell, or a plurality of battery cells connectedin series into a module and each module connected in series or inparallel with other modules. Configuration of an energy sourcecontaining connected modules may be designed to meet an energy or powerrequirement and may be designed to fit within a designated footprint inan electric aircraft in which eVTOL aircraft 200 may be incorporated.

In an embodiment, and still referring to FIG. 2 , an energy source maybe used to provide a steady supply of electrical power to a load overthe course of a flight by a vehicle or other electric aircraft. Forexample, the energy source may be capable of providing sufficient powerfor “cruising” and other relatively low-energy phases of flight. Anenergy source may also be capable of providing electrical power for somehigher-power phases of flight as well, particularly when the energysource is at a high SOC, as may be the case for instance during takeoff.In an embodiment, the energy source may be capable of providingsufficient electrical power for auxiliary loads including withoutlimitation, lighting, navigation, communications, de-icing, steering orother systems requiring power or energy. Further, the energy source maybe capable of providing sufficient power for controlled descent andlanding protocols, including, without limitation, hovering descent orrunway landing. As used herein the energy source may have high powerdensity where the electrical power an energy source can usefully produceper unit of volume and/or mass is relatively high. The electrical poweris defined as the rate of electrical energy per unit time. An energysource may include a device for which power that may be produced perunit of volume and/or mass has been optimized, at the expense of themaximal total specific energy density or power capacity, during design.Non-limiting examples of items that may be used as at least an energysource may include batteries used for starting applications including Liion batteries which may include NCA, NMC, Lithium iron phosphate(LiFePO4) and Lithium Manganese Oxide (LMO) batteries, which may bemixed with another cathode chemistry to provide more specific power ifthe application requires Li metal batteries, which have a lithium metalanode that provides high power on demand, Li ion batteries that have asilicon or titanite anode, energy source may be used, in an embodiment,to provide electrical power to an electric aircraft or drone, such as anelectric aircraft vehicle, during moments requiring high rates of poweroutput, including without limitation takeoff, landing, thermal de-icingand situations requiring greater power output for reasons of stability,such as high turbulence situations, as described in further detailbelow. A battery may include, without limitation a battery using nickelbased chemistries such as nickel cadmium or nickel metal hydride, abattery using lithium ion battery chemistries such as a nickel cobaltaluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate(LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide(LMO), a battery using lithium polymer technology, lead-based batteriessuch as without limitation lead acid batteries, metal-air batteries, orany other suitable battery. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source. Batteries may beprimary or secondary or a combination of both. Additional disclosurerelated to batteries and battery modules may be found in co-owned U.S.patent applications entitled “SYSTEM AND METHOD FOR HIGH ENERGY DENSITYBATTERY MODULE” and “SYSTEMS AND METHODS FOR RESTRICTING POWER TO A LOADTO PREVENT ENGAGING CIRCUIT PROTECTION DEVICE FOR AN AIRCRAFT,” havingU.S. patent application Ser. Nos. 16/948,140 and 16/590,496respectively; the entirety of both applications are incorporated hereinby reference.

Still referring to FIG. 2 , an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Themodule may include batteries connected in parallel or in series or aplurality of modules connected either in series or in parallel designedto deliver both the power and energy requirements of the application.Connecting batteries in series may increase the voltage of at least anenergy source which may provide more power on demand. High voltagebatteries may require cell matching when high peak load is needed. Asmore cells are connected in strings, there may exist the possibility ofone cell failing which may increase resistance in the module and reducethe overall power output as the voltage of the module may decrease as aresult of that failing cell. Connecting batteries in parallel mayincrease total current capacity by decreasing total resistance, and italso may increase overall amp-hour capacity. The overall energy andpower outputs of at least an energy source may be based on theindividual battery cell performance or an extrapolation based on themeasurement of at least an electrical parameter. In an embodiment wherethe energy source includes a plurality of battery cells, the overallpower output capacity may be dependent on the electrical parameters ofeach individual cell. If one cell experiences high self-discharge duringdemand, power drawn from at least an energy source may be decreased toavoid damage to the weakest cell. The energy source may further include,without limitation, wiring, conduit, housing, cooling system and batterymanagement system. Persons skilled in the art will be aware, afterreviewing the entirety of this disclosure, of many different componentsof an energy source. Exemplary energy sources are disclosed in detail inU.S. patent application Ser. Nos. 16/948,157 and 16/048,140 bothentitled “SYSTEM AND METHOD FOR HIGH ENERGY DENSITY BATTERY MODULE” byS. Donovan et al., which are incorporated in their entirety herein byreference.

Still referring to FIG. 2 , eVTOL aircraft 200 may include at least alongitudinal thrust component 216. As used in this disclosure a“longitudinal thrust component” is a flight component that is mountedsuch that the component thrusts the flight component through a medium.As a non-limiting example, longitudinal thrust flight component 216 mayinclude a pusher flight component such as a pusher propeller, a pushermotor, a pusher propulsor, and the like. Additionally, or alternatively,pusher flight component may include a plurality of pusher flightcomponents. As a further non-limiting example, longitudinal thrustflight component may include a puller flight component such as a pullerpropeller, a puller motor, a puller propulsor, and the like.Additionally, or alternatively, puller flight component may include aplurality of puller flight components.

Now referring to FIG. 3 , an exemplary embodiment 300 of a flightcontroller 104 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 104 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 104may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 104 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 3 , flight controller 104may include a signal transformation component 304. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 304 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component304 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 304 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 304 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 304 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 3 , signal transformation component 304 may beconfigured to optimize an intermediate representation 308. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 304 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 304 may optimizeintermediate representation 308 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 304 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 304 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 104. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 304 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 3 , flight controller 104may include a reconfigurable hardware platform 316. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 316 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 3 , reconfigurable hardware platform 316 mayinclude a logic component 316. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 316 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 316 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 316 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 316 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 316 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 308. Logiccomponent 316 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 104. Logiccomponent 316 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 316 may beconfigured to execute the instruction on intermediate representation 308and/or output language. For example, and without limitation, logiccomponent 316 may be configured to execute an addition operation onintermediate representation 308 and/or output language.

In an embodiment, and without limitation, logic component 316 may beconfigured to calculate a flight element 320. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 320 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 320 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 320 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 3 , flight controller 104 may include a chipsetcomponent 324. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 324 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 316 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 324 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 316 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 324 maymanage data flow between logic component 316, memory cache, and a flightcomponent 328. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 328 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component328 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 324 may be configured to communicate witha plurality of flight components as a function of flight element 320.For example, and without limitation, chipset component 324 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 3 , flight controller 104may be configured generate an autonomous function, wherein an autonomousfunction is a mode and/or function of flight controller 104 thatcontrols aircraft automatically as described above, in reference to FIG.1 . For example, and without limitation, autonomous function may performone or more aircraft maneuvers, take offs, landings, altitudeadjustments, flight leveling adjustments, turns, climbs, and/ordescents. As a further non-limiting example, autonomous function mayadjust one or more airspeed velocities, thrusts, torques, and/orgroundspeed velocities. As a further non-limiting example, autonomousfunction may perform one or more flight path corrections and/or flightpath modifications as a function of flight element 320. In anembodiment, autonomous function may include one or more modes ofautonomy such as, but not limited to, autonomous mode, semi-autonomousmode, and/or non-autonomous mode. As used in this disclosure “autonomousmode” is a mode that automatically adjusts and/or controls aircraftand/or the maneuvers of aircraft in its entirety. For example,autonomous mode may denote that flight controller 104 will adjust theaircraft. As used in this disclosure a “semi-autonomous mode” is a modethat automatically adjusts and/or controls a portion and/or section ofaircraft. For example, and without limitation, semi-autonomous mode maydenote that a pilot will control the propulsors, wherein flightcontroller 104 will control the ailerons and/or rudders. As used in thisdisclosure “non-autonomous mode” is a mode that denotes a pilot willcontrol aircraft and/or maneuvers of aircraft in its entirety.

In an embodiment, and still referring to FIG. 3 , flight controller 104may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 320 and a pilot signal332 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 332may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 332 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 332may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 332 may include an explicitsignal directing flight controller 104 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 332 may include an implicit signal, wherein flight controller 104detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 332 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 332 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 332 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 332 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal332 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 3 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 104 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 104.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 3 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 104 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 3 , flight controller 104 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 104. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 104 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 104 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 3 , flight controller 104 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 3 , flight controller 104may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller104 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 104 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 104 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 3 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 328. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 3 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 104. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 308 and/or output language from logiccomponent 316, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 3 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 3 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 3 , flight controller 104 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 104 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 3 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 3 , flight controller may include asub-controller 336. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 104 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 336may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 336 may include any component of any flightcontroller as described above. Sub-controller 336 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 336may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 336 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 3 , flight controller may include aco-controller 340. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 104 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 340 mayinclude one or more controllers and/or components that are similar toflight controller 104. As a further non-limiting example, co-controller340 may include any controller and/or component that joins flightcontroller 104 to distributer flight controller. As a furthernon-limiting example, co-controller 340 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 104 to distributed flight control system. Co-controller 340may include any component of any flight controller as described above.Co-controller 340 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 3 , flightcontroller 104 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 104 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 404 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 408 given data provided as inputs 412;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 400 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 404. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 404. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process428 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Now referring to FIG. 5 , a method 500 for producing a control signal ofan electric vertical take-off and landing (eVTOL) aircraft isillustrated. At step 505, a flight controller 104 obtains a requestedaircraft force 108. Flight controller 104 includes any of the flightcontroller 104 as described above, in reference to FIGS. 1-4 . Requestedaircraft force 108 includes any of the requested aircraft force 108 asdescribed above, in reference to FIGS. 1-4 .

Still referring to FIG. 5 , at step 510, flight controller 104 generatesan optimal command mix 112 as a function of requested aircraft force108. Optimal command mix 112 includes any of the optimal command mix 112as described above, in reference to FIGS. 1-4 . Optimal command mix 112includes a plurality of commands to a plurality of actuators. Commandsincludes any of the commands as described above, in reference to FIGS.1-4 . Actuator includes any of the actuator as described above, inreference to FIGS. 1-4 . Flight controller 104 generates optimal commandmix 112 as a function of receiving an ideal actuator model 116 includesat least a performance parameter 120. Ideal actuator model 116 includesany of the ideal actuator model 116 as described above, in reference toFIGS. 1-4 . Performance parameter 120 includes any of the performanceparameter 120 as described above, in reference to FIGS. 1-4 . Flightcontroller 104 generates optimal command mix 112 as a function ofproducing a model datum 124 as a function of ideal actuator model 116.Model datum 124 includes any of the model datum 124 as described above,in reference to FIGS. 1-4 . Flight controller 104 generates optimalcommand mix 112 as a function of requested aircraft force 108 and modeldatum 124.

Still referring to FIG. 5 , at step 515, flight controller 104 producesa control signal 128 as a function of optimal command mix 112. Controlsignal 128 includes any of the control signal 128 as described above, inreference to FIGS. 1-4 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 604 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 604 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 604 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve systems andmethods according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A method for producing a signal of an electricvertical take-off and landing (eVTOL) aircraft, the method comprising:obtaining, by a flight controller, a requested aircraft force;generating, by the flight controller, an optimal command mix, whereinthe optimal command mix includes a plurality of commands to a pluralityof actuators as a function of the requested aircraft force, whereingenerating the optimal command mix further comprises: receiving an idealactuator model including at least a performance parameter; producing amodel datum as a function of the ideal actuator model; and generatingthe optimal command mix as a function of the requested aircraft forceand the model datum; and producing, by the flight controller, a controlsignal as a function of the optimal command mix, wherein the controlsignal comprises a command directed to the plurality of actuatorscoupled to a plurality of flight components, respectively, of the eVTOLaircraft, wherein the command causes the eVTOL aircraft to perform amaneuver.
 2. The method of claim 1, wherein obtaining the requestedaircraft force further comprises receiving a pilot input.
 3. The methodof claim 1, wherein generating the optimal command mix further comprisesreceiving a sensor input.
 4. The method of claim 1, wherein generatingthe optimal command mix further comprises determining an effectivityelement.
 5. The method of claim 4, wherein determining the effectivityelement further comprises identifying an effectiveness of a flightcomponent.
 6. The method of claim 1, wherein generating the optimalcommand mix further comprises dynamically varying the plurality ofactuators as a function of the requested aircraft force.
 7. The methodof claim 1, wherein generating the optimal command mix comprisesgeneration the optimal command mix as a function of the requestedaircraft force and the model datum.
 8. The method of claim 1, whereinproducing the control signal further comprises producing a first commanddirected to a first actuator of the plurality of actuators coupled to afirst flight component of the eVTOL, and wherein the first flightcomponent is configured to provide thrust to the eVTOL.
 9. The method ofclaim 8, wherein directing the first command to a first actuator furthercomprises directing the first command to a longitudinal thrustcomponent, and wherein the longitudinal thrust component is configuredto provide a forward thrust to the eVTOL.
 10. The method of claim 1,wherein producing a control signal further comprises producing a firstcommand directed to a first actuator of the plurality of actuatorscoupled to a first flight component of the eVTOL, and wherein the firstflight component is configured to produce lift for the eVTOL.
 11. Asystem for producing a signal of an electric vertical take-off andlanding (eVTOL) aircraft, the system comprising: one or more processorsconfigured to: obtain, by a flight controller, a requested aircraftforce; generate, by the flight controller, an optimal command mix,wherein the optimal command mix includes a plurality of commands to aplurality of actuators as a function of the requested aircraft force,wherein generating the optimal command mix further comprises: receivingan ideal actuator model including at least a performance parameter;producing a model datum as a function of the ideal actuator model; andgenerating the optimal command mix; and produce, by the flightcontroller, a control signal as a function of the optimal command mix,wherein the control signal comprises a command directed to a pluralityof actuators coupled to a plurality of flight components of the eVTOLaircraft, wherein the command causes the eVTOL aircraft to perform amaneuver.
 12. The system of claim 11, wherein obtaining the requestedaircraft force further comprises receiving a pilot input.
 13. The systemof claim 11, wherein generating the optimal command mix furthercomprises receiving a sensor input.
 14. The system of claim 11, whereingenerating the optimal command mix further comprises determining aneffectivity element.
 15. The system of claim 14, wherein determining theeffectivity element further comprises identifying an effectiveness of aflight component.
 16. The system of claim 11, wherein generating theoptimal command mix further comprises dynamically varying the pluralityof actuators as a function of the requested aircraft force.
 17. Thesystem of claim 11, wherein generating the optimal command mix comprisesgeneration the optimal command mix as a function of the requestedaircraft force and the model datum.
 18. The system of claim 11, whereinproducing the control signal further comprises producing a first commanddirected to a first actuator of the plurality of actuators coupled to afirst flight component of the eVTOL, and wherein the first flightcomponent is configured to provide a thrust to the eVTOL.
 19. The systemof claim 18, wherein directing the first command to a first actuatorfurther comprises directing the first command to a longitudinal thrustcomponent, and wherein the longitudinal thrust component is configuredto provide a forward thrust to the eVTOL.
 20. The system of claim 11,wherein producing a control signal further comprises producing a firstcommand directed to a first actuator of the plurality of actuatorscoupled to a first flight component of the eVTOL, and wherein the firstflight component is configured to produce lift for the eVTOL.